AI-Driven Classification of Tsunami-Generating Earthquakes: Harnessing Random Forest, SVM, and Logistic Regression for Early Detection

IF 6.3 2区地球科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2026-02-12 DOI:10.1109/JSTARS.2026.3664318

Imen Ziadi;Nejla Essaddi;Mongi Besbes

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引用次数: 0

Abstract

Predicting whether an earthquake will generate a tsunami is critical for early warning systems and disaster mitigation. In this study, we present an AI-driven approach to classify earthquakes as tsunami-generating or nontsunami events. We utilize three machine learning models—random forest, support vector machine, and logistic regression—trained on USGS earthquake data from 2015 to 2025, considering features, such as magnitude, depth, latitude, and longitude. Our exploratory data analysis highlights key correlations and feature distributions, while model evaluation demonstrates high classification performance, with random forest achieving up to 91% accuracy. We further investigate feature importance and provide ROC curves and confusion matrices for comparative analysis. The results show that AI-driven classification can effectively support early warning systems, offering a scalable and data-informed tool for seismic hazard assessment.

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人工智能驱动的海啸地震分类：利用随机森林、支持向量机和逻辑回归进行早期检测

预测地震是否会引发海啸对早期预警系统和减灾至关重要。在本研究中，我们提出了一种人工智能驱动的方法，将地震分类为引发海啸或非海啸事件。我们利用三种机器学习模型——随机森林、支持向量机和逻辑回归，对2015年至2025年的USGS地震数据进行训练，考虑震级、深度、纬度和经度等特征。我们的探索性数据分析突出了关键相关性和特征分布，而模型评估显示了高分类性能，随机森林达到了91%的准确率。我们进一步研究特征的重要性，并提供ROC曲线和混淆矩阵进行比较分析。结果表明，人工智能驱动的分类可以有效地支持早期预警系统，为地震灾害评估提供可扩展和数据知情的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 地学-成像科学与照相技术

CiteScore

9.30

自引率

10.90%

发文量

563

审稿时长

4.7 months

期刊介绍： The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.